{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import string\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tbpath = \"../../fits/\"\n",
    "productpath = \"../../postfit_derivatives/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\"fulllinearmodel_fit_table.csv\",\"reducedlinearmodelNegBinom_fit_table.csv\",\n",
    "          \"reducedlinearmodelq0_fit_table.csv\",\"reducedlinearmodelq0ctime_fit_table.csv\",\n",
    "         \"nonlinearmodelq0ctime_fit_table.csv\",\"nonlinearmodel_fit_table.csv\"]\n",
    "# model_hash = {}\n",
    "# k = -1\n",
    "# for model in models:\n",
    "#     k += 1\n",
    "#     model_hash[model] = string.ascii_uppercase[k]\n",
    "\n",
    "# df = pd.DataFrame.from_dict(model_hash, orient='index')\n",
    "# df.to_csv('../postmodel_derivatives/model_hash.csv', header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rois = []\n",
    "for model in models:\n",
    "    df = pd.read_csv(tbpath + model) #get rois in all tables (some may have failed)\n",
    "    rois += list(df.roi.unique())\n",
    "\n",
    "    \n",
    "rois = list(set(rois))\n",
    "roi_us = np.sort([i for i in rois if i[:2]=='US'])[::-1]\n",
    "roi_other = np.sort([i for i in rois if i[:2]!='US'])[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'q'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'q'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-00810d4c56db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtbpath\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m#         try:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m&\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quantile'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;31m#         except:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#             print()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1416\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1419\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    803\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    927\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_label_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 929\u001b[0;31m                 \u001b[0msection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m                 \u001b[0;31m# we have yielded a scalar ?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1848\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1849\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1850\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no slices here, handle elsewhere\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3727\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3728\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3729\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3730\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3731\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2978\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2979\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2980\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2981\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'q'"
     ]
    }
   ],
   "source": [
    "theta = \"R0\"\n",
    "df2 = pd.DataFrame(columns=[i.split('_fit_table.csv')[0] for i in models])\n",
    "k = 0\n",
    "for roi in rois:\n",
    "#     print(roi)\n",
    "    x = []\n",
    "    for model in models:\n",
    "        df = pd.read_csv(datapath + model)\n",
    "        try:\n",
    "            x.append(df.loc[(df.roi==roi)&(df['quantile']==0.5), theta].values[0])\n",
    "        except:\n",
    "            print()\n",
    "    if len(x)==len(models):\n",
    "        k += 1\n",
    "#         print(x)\n",
    "        df2.loc[k] = x "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = sns.pairplot(df2)\n",
    "f.fig.suptitle(theta, fontsize=18)\n",
    "plt.savefig(productpath + 'pairsplot_'+theta+'.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
